Overview

Dataset statistics

Number of variables10
Number of observations752
Missing cells388
Missing cells (%)5.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.9 KiB
Average record size in memory80.2 B

Variable types

NUM9
BOOL1

Reproduction

Analysis started2020-08-05 18:43:09.164852
Analysis finished2020-08-05 18:43:25.979447
Duration16.81 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

BloodPressure has 28 (3.7%) missing values Missing
Insulin has 360 (47.9%) missing values Missing
df_index has unique values Unique
Pregnancies has 108 (14.4%) zeros Zeros

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct count752
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean385.01462765957444
Minimum0
Maximum767
Zeros1
Zeros (%)0.1%
Memory size5.9 KiB
2020-08-05T11:43:26.072790image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.55
Q1194.75
median385.5
Q3577.25
95-th percentile729.45
Maximum767
Range767
Interquartile range (IQR)382.5

Descriptive statistics

Standard deviation221.5469605
Coefficient of variation (CV)0.575424788
Kurtosis-1.198922796
Mean385.0146277
Median Absolute Deviation (MAD)191.5
Skewness-0.003831991044
Sum289531
Variance49083.05571
2020-08-05T11:43:26.230846image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
76710.1%
 
25310.1%
 
26210.1%
 
26110.1%
 
26010.1%
 
25910.1%
 
25810.1%
 
25710.1%
 
25610.1%
 
25510.1%
 
Other values (742)74298.7%
 
ValueCountFrequency (%) 
010.1%
 
110.1%
 
210.1%
 
310.1%
 
410.1%
 
ValueCountFrequency (%) 
76710.1%
 
76610.1%
 
76510.1%
 
76410.1%
 
76310.1%
 

Pregnancies
Real number (ℝ≥0)

ZEROS

Distinct count17
Unique (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.851063829787234
Minimum0
Maximum17
Zeros108
Zeros (%)14.4%
Memory size5.9 KiB
2020-08-05T11:43:26.355889image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.375189327
Coefficient of variation (CV)0.8764303778
Kurtosis0.1705680019
Mean3.85106383
Median Absolute Deviation (MAD)2
Skewness0.9072902384
Sum2896
Variance11.39190299
2020-08-05T11:43:26.473837image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
113217.6%
 
010814.4%
 
210113.4%
 
3749.8%
 
4689.0%
 
5557.3%
 
6486.4%
 
7445.9%
 
8374.9%
 
9283.7%
 
Other values (7)577.6%
 
ValueCountFrequency (%) 
010814.4%
 
113217.6%
 
210113.4%
 
3749.8%
 
4689.0%
 
ValueCountFrequency (%) 
1710.1%
 
1510.1%
 
1420.3%
 
13101.3%
 
1291.2%
 

Glucose
Real number (ℝ≥0)

Distinct count135
Unique (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.94148936170212
Minimum44.0
Maximum199.0
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-08-05T11:43:26.594583image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile80
Q199.75
median117
Q3141
95-th percentile181
Maximum199
Range155
Interquartile range (IQR)41.25

Descriptive statistics

Standard deviation30.60119806
Coefficient of variation (CV)0.2509498467
Kurtosis-0.2954030665
Mean121.9414894
Median Absolute Deviation (MAD)20
Skewness0.5226732772
Sum91700
Variance936.4333229
2020-08-05T11:43:26.716131image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
100172.3%
 
99172.3%
 
129141.9%
 
111141.9%
 
106141.9%
 
112131.7%
 
108131.7%
 
95131.7%
 
125131.7%
 
109121.6%
 
Other values (125)61281.4%
 
ValueCountFrequency (%) 
4410.1%
 
5610.1%
 
5720.3%
 
6110.1%
 
6210.1%
 
ValueCountFrequency (%) 
19910.1%
 
19810.1%
 
19740.5%
 
19630.4%
 
19520.3%
 

BloodPressure
Real number (ℝ≥0)

MISSING

Distinct count46
Unique (%)6.4%
Missing28
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean72.40055248618785
Minimum24.0
Maximum122.0
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-08-05T11:43:26.843353image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile52
Q164
median72
Q380
95-th percentile91.7
Maximum122
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.37987032
Coefficient of variation (CV)0.1709913792
Kurtosis0.9228827146
Mean72.40055249
Median Absolute Deviation (MAD)8
Skewness0.1376292303
Sum52418
Variance153.2611892
2020-08-05T11:43:26.940468image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
70577.6%
 
74516.8%
 
78456.0%
 
72445.9%
 
68435.7%
 
64425.6%
 
80395.2%
 
76395.2%
 
60374.9%
 
62344.5%
 
Other values (36)29339.0%
 
ValueCountFrequency (%) 
2410.1%
 
3020.3%
 
3810.1%
 
4010.1%
 
4440.5%
 
ValueCountFrequency (%) 
12210.1%
 
11410.1%
 
11030.4%
 
10820.3%
 
10630.4%
 

SkinThickness
Real number (ℝ≥0)

Distinct count51
Unique (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.172284644194754
Minimum7.0
Maximum99.0
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-08-05T11:43:27.227448image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q125
median29.17228464
Q332
95-th percentile44
Maximum99
Range92
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.852102582
Coefficient of variation (CV)0.303442212
Kurtosis5.344832133
Mean29.17228464
Median Absolute Deviation (MAD)3.827715356
Skewness0.8162601036
Sum21937.55805
Variance78.35972012
2020-08-05T11:43:27.328549image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
29.1722846421829.0%
 
32304.0%
 
30273.6%
 
27233.1%
 
23202.7%
 
33202.7%
 
28202.7%
 
18202.7%
 
31192.5%
 
19182.4%
 
Other values (41)33744.8%
 
ValueCountFrequency (%) 
720.3%
 
820.3%
 
1050.7%
 
1160.8%
 
1270.9%
 
ValueCountFrequency (%) 
9910.1%
 
6310.1%
 
6010.1%
 
5610.1%
 
5420.3%
 

Insulin
Real number (ℝ≥0)

MISSING

Distinct count184
Unique (%)46.9%
Missing360
Missing (%)47.9%
Infinite0
Infinite (%)0.0%
Mean156.05612244897958
Minimum14.0
Maximum846.0
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-08-05T11:43:27.430050image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile42.55
Q176.75
median125.5
Q3190
95-th percentile396.5
Maximum846
Range832
Interquartile range (IQR)113.25

Descriptive statistics

Standard deviation118.8416898
Coefficient of variation (CV)0.7615317355
Kurtosis6.356505089
Mean156.0561224
Median Absolute Deviation (MAD)54.5
Skewness2.165116186
Sum61174
Variance14123.34723
2020-08-05T11:43:27.532592image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
105111.5%
 
14091.2%
 
13091.2%
 
12081.1%
 
9470.9%
 
10070.9%
 
18070.9%
 
11060.8%
 
13560.8%
 
11560.8%
 
Other values (174)31642.0%
 
(Missing)36047.9%
 
ValueCountFrequency (%) 
1410.1%
 
1510.1%
 
1610.1%
 
1820.3%
 
2210.1%
 
ValueCountFrequency (%) 
84610.1%
 
74410.1%
 
68010.1%
 
60010.1%
 
57910.1%
 

BMI
Real number (ℝ≥0)

Distinct count246
Unique (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.45465425531915
Minimum18.2
Maximum67.1
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-08-05T11:43:27.681619image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.2
Q127.5
median32.3
Q336.6
95-th percentile44.5
Maximum67.1
Range48.9
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.928926198
Coefficient of variation (CV)0.2134956097
Kurtosis0.8748420787
Mean32.45465426
Median Absolute Deviation (MAD)4.6
Skewness0.5968157768
Sum24405.9
Variance48.01001826
2020-08-05T11:43:27.803642image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
32121.6%
 
31.2121.6%
 
31.6121.6%
 
32.4101.3%
 
33.3101.3%
 
30.891.2%
 
32.891.2%
 
32.991.2%
 
30.191.2%
 
34.281.1%
 
Other values (236)65286.7%
 
ValueCountFrequency (%) 
18.230.4%
 
18.410.1%
 
19.110.1%
 
19.310.1%
 
19.410.1%
 
ValueCountFrequency (%) 
67.110.1%
 
59.410.1%
 
57.310.1%
 
5510.1%
 
53.210.1%
 

DiabetesPedigreeFunction
Real number (ℝ≥0)

Distinct count511
Unique (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47305053191489366
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-08-05T11:43:27.959432image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.141
Q10.244
median0.377
Q30.6275
95-th percentile1.13105
Maximum2.42
Range2.342
Interquartile range (IQR)0.3835

Descriptive statistics

Standard deviation0.3301080525
Coefficient of variation (CV)0.6978283085
Kurtosis5.592024879
Mean0.4730505319
Median Absolute Deviation (MAD)0.17
Skewness1.9040609
Sum355.734
Variance0.1089713263
2020-08-05T11:43:28.108458image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.25860.8%
 
0.25460.8%
 
0.23850.7%
 
0.20750.7%
 
0.25950.7%
 
0.26850.7%
 
0.69240.5%
 
0.26340.5%
 
0.19740.5%
 
0.55140.5%
 
Other values (501)70493.6%
 
ValueCountFrequency (%) 
0.07810.1%
 
0.08410.1%
 
0.08520.3%
 
0.08820.3%
 
0.08910.1%
 
ValueCountFrequency (%) 
2.4210.1%
 
2.32910.1%
 
2.28810.1%
 
2.13710.1%
 
1.89310.1%
 

Age
Real number (ℝ≥0)

Distinct count52
Unique (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.3125
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Memory size5.9 KiB
2020-08-05T11:43:28.227049image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.70939523
Coefficient of variation (CV)0.3515015455
Kurtosis0.6166577132
Mean33.3125
Median Absolute Deviation (MAD)7
Skewness1.116815734
Sum25051
Variance137.1099368
2020-08-05T11:43:28.339772image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
22689.0%
 
21597.8%
 
25476.2%
 
24456.0%
 
23385.1%
 
28354.7%
 
26324.3%
 
27324.3%
 
29293.9%
 
31243.2%
 
Other values (42)34345.6%
 
ValueCountFrequency (%) 
21597.8%
 
22689.0%
 
23385.1%
 
24456.0%
 
25476.2%
 
ValueCountFrequency (%) 
8110.1%
 
7210.1%
 
7010.1%
 
6910.1%
 
6810.1%
 

Outcome
Boolean

Distinct count2
Unique (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
0
488
1
264
ValueCountFrequency (%) 
048864.9%
 
126435.1%
 

Interactions

2020-08-05T11:43:13.452739image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:13.639320image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:13.788181image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:13.942863image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:14.083168image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:14.220556image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:14.348101image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:14.490091image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:14.632889image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:14.778077image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:14.925321image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:15.071134image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:15.221550image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:15.361595image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:15.506896image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:15.644003image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:15.778246image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:15.923800image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:16.068661image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:16.220030image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:16.998415image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:17.165923image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:17.314283image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:17.462274image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:17.603055image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:17.743056image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:17.896703image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:18.054402image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:18.202340image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:18.343950image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:18.494892image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:18.625207image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:18.761389image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:18.885859image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:19.014956image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:19.147325image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:19.283196image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:19.417437image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:19.549737image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:19.687290image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:19.811864image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:19.939408image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:20.062073image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:20.194129image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:20.326878image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:20.465751image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:20.590919image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:20.719965image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:20.845381image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:20.957117image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:21.073471image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:21.183165image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:21.289310image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:21.402729image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:21.517850image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:21.778702image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:21.911515image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:22.045057image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:22.172574image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:22.296486image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:22.409396image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:22.530535image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:22.659001image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:22.787549image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:22.928172image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:23.070313image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:23.218842image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:23.353356image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:23.484874image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:23.599899image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:23.728793image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:23.860522image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:23.995636image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:24.140816image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:24.278713image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:24.420282image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:24.555161image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:24.682983image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:24.803698image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:24.935299image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:25.073246image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-08-05T11:43:28.465590image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-05T11:43:28.680102image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-05T11:43:28.892478image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-05T11:43:29.109841image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-05T11:43:25.321641image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:25.590463image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:25.756421image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-05T11:43:25.865704image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

df_indexPregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
006148.072.035.000000NaN33.60.627501
11185.066.029.000000NaN26.60.351310
228183.064.029.172285NaN23.30.672321
33189.066.023.00000094.028.10.167210
440137.040.035.000000168.043.12.288331
555116.074.029.172285NaN25.60.201300
66378.050.032.00000088.031.00.248261
7710115.0NaN29.172285NaN35.30.134290
882197.070.045.000000543.030.50.158531
9104110.092.029.172285NaN37.60.191300

Last rows

df_indexPregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
7427581106.076.029.172285NaN37.50.197260
7437596190.092.029.172285NaN35.50.278661
744760288.058.026.00000016.028.40.766220
7457619170.074.031.000000NaN44.00.403431
746762989.062.029.172285NaN22.50.142330
74776310101.076.048.000000180.032.90.171630
7487642122.070.027.000000NaN36.80.340270
7497655121.072.023.000000112.026.20.245300
7507661126.060.029.172285NaN30.10.349471
751767193.070.031.000000NaN30.40.315230